Using FAF Data in Economic Analysis/Case Study: North Dakota
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Case Study: North Dakota Using FAF Data in Economic Analysis June 26, 2014 EunSu Lee Associate Research Fellow, Upper Great Plains Transportation Institute North Dakota State University, Fargo, ND
UGPTI researcher EunSu Lee uses work in North Dakota as an example of how practitioners can use Freight Analysis Framework (FAF) data to support economic analysis, such as cost-benefit studies, freight investment scenarios and other activities.
TRANSCRIPT
Case Study: North Dakota Using FAF Data in Economic Analysis
June 26, 2014
EunSu Lee Associate Research Fellow, Upper Great Plains Transportation Institute
North Dakota State University, Fargo, ND
Presenter
Presentation Notes
Dan gave us a great demonstration regarding how to use FAF economic data. I am focusing on a case study to show how to utilize FAF data for the state needs analysis using economic analysis tools, instead of introducing FAF economic data. Currently, UGPTI is developing the 3rd generation of statewide travel demand modeling and needs analysis. This presentation is a modified version of the report available from UGPTI Website and the 2nd generation of statewide travel demand modeling and needs analysis, which was finished in 2012.
Agenda • Background • Objective of the case study • Economic analysis and FAF • Understanding FAF • Why FAF? • Incorporating FAF • Results • Q & A
Presenter
Presentation Notes
I am going to discuss…..and followed by Results and Q&A session.
Background • Boom of oil & gas industries, changes of
agricultural logistics pattern, and increase of other baseline traffic
• Road infrastructure needs for a 20-year planning horizon
• Needs for travel demand modeling (TDM) and economic analysis
Presenter
Presentation Notes
In the recent years, North Dakota has been experiencing a boom of oil and gas development. 19 counties produce around a million barrels of oil per day. Drilling a new oil generates 2300 truck trips. Agricultural goods exceeded $5.5 billion in 2009. Throughout the state, there are more than 300 grain elevators, ethanol plants, and processing centers. See, production, technology, and locations also contribute to the changes of the agricultural logistics pattern. In 2010, over 763 million bushels were transported to the end users. In addition to the freight in the state, population growth also need a great transportation infrastructure. The population was 635000 in 2010 and will be 723,393 in 2013, which is 7.6% growth.
Objective of the Case Study • To support NDDOT and Counties to
identify current and future needs • Tasks
– To quantify freight flows for commodities on major highways and local roads
– To forecast future highway capacity – To estimate investments needs
Needs for Statewide Modeling • Funnel of Dynamic Economic Activities
– Increasing complexity
Empirical
Base
Forecasting
Transportation Planning
Energy
Agriculture Man
ufacturing Logis
tics
Passenger
Through
Trade
Time
Unc
erta
inty
Presenter
Presentation Notes
The funnel of dynamic economic activities in the state increases complexity of the statewide travel demand modeling and needs analysis. The degree of uncertainty for the traffic and economic forecasting gets difficult as include the future activities. It is hard to put everything in a single pot and make taste good for all in your party.
Importance of Economic Analysis • Critical rural freight corridors for goods
movement • Financial constraint • Deteriorated and insufficient
infrastructure for energy and agricultural logistics and economic activities
• Using FAF Traffic Data – Benefit Cost Analysis (BCA) – Life-Cycle Cost Analysis (LCCA)
Presenter
Presentation Notes
Importance of economic analysis should be addressed because rural freight corridors for goods movement is crucial; however, there exists financial constraint due to increasing maintenance costs and budge allocation process; deteriorated and insufficient transportation infrastructure could be barriers for effective and efficient energy and agricultural logistics and economic activities in the supply chain. Using FAF traffic data, we conducted BCA and LCCA to estimate the state and local agencies’ investment needs.
Economic Analysis Process Overview
Source: HERS-ST User Guide9
Highway Inventory Capacity NDDOT/UGPTI Network
Engineering Standards Deficiency Standards Benchmark Unit Cost User Cost Treatment
Traffic Forecasting (intermediate year file)
Cost and Treatment
Road Network
Traffic Volume
Benefit-Cost
Analysis
Life-Cycle Cost
Analysis
FAF Local trips
Presenter
Presentation Notes
Let’s go overview of the economic analysis process we have done with FAF data. It is a kind of reverse engineering to explain the process. We utilized the BCA and LCCA. Cost and treatment, road network, and traffic volume are critical input sources for the economic analysis. Especially, we are focusing on traffic volume in this presentation. To forecast the future traffic, FAF traffic and local trips are sourced. I am going to explain the FAF traffic and local trips in the next slides.
State Traffic Model • Intra-zonal Movement
– UGPTI Report from www.ugpti.org
Economic Analysis Tools
Agriculture
Manufacturing
Logistics
Passenger
Through Trade
Inter-state
Energy
Presenter
Presentation Notes
The local trips are intra-zonal movement. If you see the FAF boundaries, the state of North Dakota has single zone, so the intra zonal movement cannot be estimated for each commodities. Thus, we estimated with our own travel demand modeling including such activities as energy development, agricultural activities, manufacturing and logistics as well as passenger travel. However, long distance trips such as through traffic, interstate freight movement, and international trade were still in the black box. So, we tried to open the box by using FAF traffic.
Let’s take a look at traffic flow patterns in FAF. The FAF traffic has two groups: Non-FAF and FAF group. Non-FAF represents local truck traffic, which is internal-internal trips, while all other long distance trips are part of FAF. Long-distance inbound freights are originated from other domestic origins to the state, which is external-internal trips. Freights originated from the state and destined to other domestic destinations are domestic outbound freights, which called internal-external trips. The state of North Dakota is located by Saskatchewan and Manitoba provinces in Canada and shows active international trade. The imported goods from foreign origins to the state is external-internal trips, while the exported goods from the state and to foreign destinations (i.e. foreign trade partners) is internal-external trips. Furthermore, there are some freights originated from domestic or foreign origins to domestic or foreign destinations passing though the state highway systems, which called through traffic. The through traffic is external-external trips. In the case study, we replaced the non-faf traffic with our estimates, and adopted the faf traffic from FAF database.
What FAF does and does NOT do • What the FAF does
– Indicates states’ and localities’ major trading partners, plus volumes and sources of traffic passing through their jurisdictions at corridor level
– Shows truck tonnage and number of trucks on the network, particularly in regions with multiple routes or significant local traffic between major centers of freight activity
• What the FAF does NOT – Show local detail or temporal variation in freight flows – Provide local data to support local applications
Presenter
Presentation Notes
To use the FAF output, we should understand what FAF does and does not. Dr. Peter Bang explained the details in the first quarterly FAF seminar. I will go through that again for our case study. The FAF indicates states’ and localities’ major trading partners, and estimates volumes and sources of traffic passing through the state of North Dakota at corridor level. The FAF shows truck tonnage and number of trucks on network, especially in regions with multiple routes or significant local traffic between major centers of freight activity such as national wide intermodal terminals. However, FAF does not show local detail or temporal variation in freight flows. For example, the majority of agricultural traffic is generated during planting and harvest seasons. But the FAF does not indicate the temporal variations. Eventually, the FAF manual recommends that FAF2 benchmark forecast would need to be supplemented by other models. Thus, we should hybrid local trips and FAF long distance trips.
Things To know About FAF3 • Geographic regions
– Single TAZ in North Dakota • Network
– Centerline without considering directions for divided highways and one-way traffic
– Not designed for the purpose of routing – Primary freight network and critical rural freight corridors
(no local roads) • Attributes
– No road condition / No pavement type • Adjustment
– Coarse space and time
Presenter
Presentation Notes
There are things to know about FAF3. In North Dakota, there is single TAZ, FAF zone, which is coarse and aggregate. The FAF network provide centerline without considering directions (i.e. west- or east-bound, north- or south-bound) and one-way traffic because the FAF network was not designed for routing purpose. The FAF network only provides primary freight network and critical rural freight corridors. No local roads are included in the network. The attributes of the network does not provide road condition and pavement types, which are critical to the life-cycle cost analysis (LCCA). You may find them from HPMS or local road networks. Thus adjustment is needed for coarse space and time.
Things To know About FAF3 • Geographic regions (FAF Zone)
Here is an map for example. The red arrow indicates North Dakota, the region of the case study. The entire state is one FAF zone.
Things To know About FAF3 • Comparison of FAF and state networks
FAF Network North Dakota GIS Hub
Presenter
Presentation Notes
Here is an comparison of FAF network and the state road network. On the left, FAF network is found without directions for the divided highways. On the right side, the network was downloaded from the North Dakota GIS portal. The networks provides divided highways, so it provides more realistic directions and routes.
Why FAF? • Comprehensive freight movements • Multimodal infrastructure • Authoritative • Affordable • Easy to use
Presenter
Presentation Notes
Nevertheless, FAF is one of the most important sources for the project. The FAF is most comprehensive freight movements for states, national, and international freights based on 2007 Commodity Flow Survey and 2008 HPMS using multimodal infrastructure. FAF is also authoritative in response to the federal agency and legislators. The network is also affordable, which is freely downloadable, opened to the public. Most of all, it is easier than other sources to use.
Presenter
Presentation Notes
The five year interval forecasting data can be found from Freight Analysis and Framework Data Tabulation Tool. As Dave demonstrated, you can specify origin, destination, unit of tons, commodity, and domestic mode. You easily can open it with any spreadsheet software. However, the O-D trips do not provides traffic on corridors and highway links. To disaggregate inter-regional flow from origin-destination database into flows between localities and to assign these flows to individual highway links are cumbersome, tedious, and time consuming. So we visit different source.
Data Download
Presenter
Presentation Notes
The FAF network database and flow assignment provide trips assigned onto the FAF network for 2007 and 2040. You can access to GIS shapefiles and FAF output. The network can be opened in ArcGIS with shapfiel format and TransCAD. You should read the metadata when you use the GIS files because the metadata provide basic information about the definition of columns, data type, accuracy, and lineage of the file.
FAF Freight: 2007
NONFAF07 FAF07
AADTT 07 AADT 07
Presenter
Presentation Notes
I visualized the FAF output in here. The ADDT of year 2007 is a combination of AADTT07 and passenger travels. As a subset of AADT07, AADTT07 consists of FAF07 and NonFAF07. FAF07 represents long distance trips, while the NONFAF07 explains local trips. The 19 oil counties in the gray region on the map generates a lot of local traffic caused by oil development activities. Gray dots repents oil wells in the region. Eastern North Dakota also indicates many local traffics due to agricultural economic activities such as sugarbeet, wheat, corns, and soybean.
Data Download
FAF – Data Dictionary AADT
AADTT FAF
NO
NFAF
Passenger
Presenter
Presentation Notes
You see the route id, which is route identifier with beginning and ending milepost for each link. That allows us to use Linear Referencing Systems.
We opened the black box using FAF to get through, inter-state, and international trade freight. A hybrid dataset of intra-zonal traffic from local economic activities and inter-zonal traffic from FAF making a full of funnel. We are ready to use them for economic analysis.
Hybrid
• Growth Rate Within the State: Non-Linear • Growth Rate of FAF07-40: Linear
Projected AADT for 2014~2032 by 2 years
Presenter
Presentation Notes
The local trips provides critical information to address capacity and congestion in the region and to estimate investment needs using economic analysis for maintenance and budget plan.
Results
Investment Needs for the Funding Periods • Average Daily Trips (ADT) • Average Daily Truck Trips (ADTT) • Truck Type and Axle Configuration • Structural Number (SN) • Cumulative ESALs • Existing Pavement Structure • Present Serviceability Rating (PSR) • Oil Module (MR) • Maximum Feasible Life with no truck
traffic • Graded Width
fact
ors
Decision Logic
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Source: www.ugpti.org
FAF
Results
Investment Needs for the Funding Periods
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Source: www.ugpti.org
Results
Estimated Funding Required for the Funding Periods
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Source: www.ugpti.org
FAF & Road Investment Planning • FAF outputs:
– Yearly or biennial traffic flows – Directional flows for major highways and
FAF was used for the economic analysis. We interpolated yearly and biennial traffic flows and disaggregated directional flows for major highways and rural freight corridors. From the case study, we found that significant amount of road investments were needed for the funding periods for paved and unpaved roads across the state.
Summary • Demonstrated how FAF is used for
Economic Analysis • Demonstrated the process of combining
local traffic and FAF traffic • Discussed the components to improve
